Panel Data Research Center at Keio University
DISCUSSION PAPER SERIES
DP2014-008
March, 2015

Childcare Policy and Regional Employment of Japanese Female Workers

Isamu Yamamoto＊
Hirotaka Ito＊
【Abstract】

This paper conducts the evaluation of the childcare policy, “General Childcare-Support
for Model-municipalities,” which was implemented for target model regions in Japan in
the 2000s. We employ the difference-in-differences methods based on regression modeland propensity score matching, to examine changes in women’s employment andworking hours in the target model regions before and after the policy’s implementation.
The estimation results imply that there has been an increase in the non-regular
workers living in the target model regions of the policy, particularly among thoseworking voluntarily as non-regular employees. This tendency is especially prominent
among married women who graduated from junior/technical college, or who care formany children under the age of six years. There is also the possibility that the program
has prompted an increase in the working hours of regular workers. However, as these
influences are not confirmed after controlling for the regional factors such as financial
index, we interpret that the effects on the employment of married women are caused notby the designation of “model region” by the government, but rather by the active
childcare supports provided by municipalities.

＊

Graduate School of Business and Commerce, Keio University

Panel Data Research Center at Keio University
Keio University

Childcare Policy and Regional Employment
of Japanese Female Workers∗
Isamu Yamamoto*

Hirotaka Ito

Keio University

Keio University
Abstract

This paper conducts the evaluation of the childcare policy, “General ChildcareSupport for Model-municipalities,” which was implemented for target model regionsin Japan in the 2000s. We employ the difference-in-differences methods based on
regression model and propensity score matching, to examine changes in women’s
employment and working hours in the target model regions before and after thepolicy’s implementation. The estimation results imply that there has been an
increase in the non-regular workers living in the target model regions of the policy,
particularly among those working voluntarily as non-regular employees. Thistendency is especially prominent among married women who graduated fromjunior/technical college, or who care for many children under the age of six years.
There is also the possibility that the program has prompted an increase in theworking hours of regular workers. However, as these influences are not confirmed
after controlling for the regional factors such as financial index, we interpret thatthe effects on the employment of married women are caused not by the designationof “model region” by the government, but rather by the active childcare supports
provided by municipalities.
Keywords: Female employment, Propensity score matching, Difference-inDifferences analysis, Childcare support* Corresponding Author: yamamoto@fbc.keio.ac.jp, Tel/Fax: +81-3-5427-1085∗

The authors deeply appreciate Yoshio Higuchi, Michio Naoi, and Yasuhiro Nohara for theirvaluable comments. We are grateful for access to the micro data from the “Keio Household Panel
Survey,” provided by the Panel Data Research Center at Keio University. Any remaining errors
are entirely our own.
1

1. Introduction
Utilization of the female workforce is an important issue in Japan, given thatcountry’s aging population and low birth rates. As of 2010, labor force participation
ratio of women aged 25–29 is 77.1% in Japan, 75.6% in the United States, and77.8% in the United Kingdom, which indicates no major differences among thethree countries. The ratio for women aged 35–39 years, however, is extremely low
in Japan (66.2%), compared to 74.1% in the United States and 76.4% in the United
Kingdom. For women aged 45–49 years, the Japanese female workforce ratio
increases to 75.8%, a level almost the same as that in the United States (76.8%)
and the United Kingdom (82.2%); however, in Japan, approximately 60% of thefemale workforce in this age range are employed as non-regular workers 1. These
trends constitute the so-called M-shaped curve for Japanese women, as Japanese
female workers tend to retire from the labor market once they reach their 30s andthen reenter the labor market in their 40s as non-regular employees.
Considering the fact that the mean age of women at their first child’s live
birth is 30.3 years, 2 we can point out that the difficulty of maintaining balance
between child-rearing and work in the Japanese labor market could be one of thereasons for the decline in the participation ratio among women in their 30s. Thus,
it should be important to explore the effective measures taken by companies andgovernments to create a system or an environment by which women can balancechild-rearing and work.

1

The data are based on the “Databook of International Labour Statistics” (Japan Institute forLabour Policy and Training, 2012) and the “Labour Force Survey” (Ministry of Internal Affairs
and Communications, 2013).

2

The data is based on the “Vital Statistics in Japan 2014 (Trends up to 2012)” (the Ministry of
Health, Labour and Welfare).
2

Typical examples of company-based child-rearing support measures include
the parental leave system, the short working hour system, and other work–lifebalance measures. Due to a recent increase in interest in work–life balance, theprovision of work environments friendly to women is under way, and empiricalstudies have examined the relationship between company-based work–life balancemeasures and female workforce utilization in companies. For example, Suruga andZhang [2003] finds that companies where parental leave systems have been explicitly
stipulated have seen an increase in the share of women employment. Kawaguchi[2011] provides an evidence that companies where work–life balance measures havebeen developed have a higher ratio of female employment. Examining the factors
to enforce female workforce utilization in companies, Yamamoto [2014] shows that
more female workers are being utilized as regular workers among companies
introducing well-developed work–life balance measures or exhibiting shorter
working hours. 3 Based on the findings of these studies, it is highly likely thatcompany-based child-rearing support and work–life balance measures help promote
female workforce utilization.
On the other hand, not many studies have been conducted with regard to
the effects of child-rearing support measures in the public sectors, especially themunicipalities, and those studies have mixed results. As a national policy toovercome the declining birthrate, the government established the “Act for Measures
to Support the Development of the Next Generation” in 2003; since then, many
child-rearing support measures—such as the “regional childcare support centerprogram” and the “Child and Child-Rearing Support Plan”—have been formulated.
At the same time, municipalities and local governments have been implementing a
3

In addition to these studies, studies on support measures for maintaining work–life balance andwomen’s employment include Higuchi [1994], Tomita [1994], Morita and Kaneko [1988], andMatsushige and Takeuchi [2008], among others.
3variety of measures, including expansions in the capacity of daycare centers, as partof government programs like the “General Childcare-Support for Modelmunicipalities” or as part of their own measures.
The “General Childcare-Support for Model-municipalities” is a childrearing support measure that was established by the government in 2004; 50
municipalities in Japan were designated as targets under this program, whichsought to provide government support to the comprehensive and active child-rearing
support systems offered by the local governments. However, previous studies inJapan have not conducted an analysis of policy evaluation for the government
programs in specific target model regions or regional child-rearing support measures
on women’s employment. Additionally, we find the mixed results in the previous
studies that examined the effect of daycare centers on the female workforce.
For example, Ohishi [2003] points out that expanding the availability of
daycare centers increases the probability of employment among mothers, andMaruyama [2001] asserts that there is a strong tendency for working women to
demand the expansion of childcare services. Higuchi, Matsuura, and Sato [2007], on
the other hand, shows that the expansion of childcare services does not always havea positive effect on women’s employment.
Therefore, this paper examines the effect of the “General ChildcareSupport for Model-municipalities” on women’s employment in Japan, based on theframework of policy evaluation analysis. Specifically, we use individual-level panel
data collected through the “Keio Household Panel Survey” (KHPS), which covers
households across Japan, to examine whether the employment rate among women
increased in the target model regions following the implementation of this policy;
we do so by conducting difference-in-differences (DD) analysis.
The major results of this paper can be summarized as follows. We find a4

tendency of increase in the non-regular employment of married women—
particularly in voluntary non-regular employment—in the target model regions(municipalities) due to the implementation of the “General Childcare-Support for
Model-municipalities.” This tendency is prominent for junior college/technicalcollege graduates, and for married women with many children under the age of six
years. We also find that this program increased the working hours of women who
work as regular employees. Focusing on the program’s structure in which thegovernment designates the “model regions (municipalities)” where the plan for thechild-rearing support measures is active and comprehensive, we then identified
whether the effects on women’s employment were produced by the efforts made by
the municipalities or by the government’s “model region” designation. Our findings
imply that it is highly likely that the increase in the non-regular employment rate
among women was caused not by the “model region” designation but by the efforts
made by the municipalities.
The structure of this paper is as follows. Section 2 provides an overview of
the “General Childcare-Support for Model-municipalities,” and introduces previous
studies on similar policies. Following this, Section 3 explains the estimation
framework, data, and variables used in this study. Section 4 outlines changes in theemployment rate of women in the model and non-model regions; it also provides
the estimation results of DD analysis. Finally, Section 5 summarizes the estimation
results and the implications.

2. General Childcare-Support for Model-municipalities
The “General Childcare-Support for Model-municipalities” is a child-rearing policy5

of the Japanese government; it was established in 2004 with the aim of“contribut[ing] to the promotion of national programs for child-rearing support bydesignating approximately 50 municipalities where comprehensive and active
measures are implemented for various child-rearing support services under themunicipality action plan formulated by the end of FY 2004.” 4 Model regions coveredby the policy were selected based on the contents of their municipality action planfor child-rearing for the first half of the term (2005–09), which was created inaccordance with the requirements of 2003’s “Act for Measures to Support theDevelopment of the Next Generation.” 5 Specifically, the structure of the program
allowed for the designation of those municipalities whose mandatory programs andoptional programs specific to child-rearing supports under the municipality’s action
plan for the first half of the term 6 were considered excellent, as “model regions.” Assuch, under the program, regions with active child-rearing support measures wereselected. Support measures—such as subsidies for the costs related to theformulation of model program promotion plans—were implemented among theselected “model region” municipalities.
Many of the government’s past child-rearing policies offered the same
service contents to all regions, and the particulars of policy operations were left toeach of the municipalities themselves. In contrast, the “General Childcare-Support
for Model-municipalities” featured a structure that called for the selection ofmunicipalities with comprehensive and active action policies, and it designated4This is excerpted from the website of Ministry of Health, Labour and Welfare(http://www.mhlw.go.jp/houdou/2004/06/h0618-6b.html).
5

Refer to http://www.mhlw.go.jp/houdou/2004/06/h0618-6a.html for details.

6

Mandatory programs include short-time daycare support programs, home child-rearing support

programs, child-rearing counseling support programs, and child-rearing support comprehensive
coordination programs, while optional programs include short-term child-rearing support
programs, home-visiting temporary daycare programs, and specific daycare programs.
6those limited regions as targets of the model programs. In this sense, we consider
this program to be different from previous ones, and so analysis of its effects isimportant.
On the other hand, the results of policy evaluation of this program must
be interpreted carefully. This is because the program designated specific regions asmodel regions, and at the time of designation, the model regions had alreadyformulated comprehensive and active action plans by which to provide child-rearing
support. Therefore, even if the increase of employment rate among the parentingage women within the model regions of this program was higher than that in otherregions, both of the following effects could be interpreted as having taken place: (1)the effects of the action plan and measures that were originally formulated by themunicipalities, and (2) additional effects produced by being designated as a model
region.
It is important to identify how municipality action plan and child-rearing
support service contents affect the balancing of childcare and work for women. At
the same time, it is also important to confirm whether the model programs andother government measures have been effective. Accordingly, in this paper, wemeasure the effects of combining these programs and measures, and also identify
any additional effects that stem from a “model program” designation by thegovernment, by controlling for the financial state of each municipality and thevariables that can affect the efficacy of child-rearing support measures.
Note, as mentioned, model regions under the “General Childcare-Support
for Model-municipalities” were designated based on their action plan for the firsthalf of the term (2005–09) under the “Act for Measures to Support the Developmentof the Next Generation”; however, municipalities continued to provide child-rearing
support services in the latter half of the term (2010–14). Therefore, we assume the7possibility that the effects of the “General Childcare-Support for Modelmunicipalities” could persist beyond the end of the program period—or from 2010
onwards, when municipality measures continued, based on the action plan for theterm’s latter half. In any case, that later plan naturally followed the action plan for
the term’s first half.
As described in the previous section, no studies on Japan’s “GeneralChildcare-Support for Model-municipalities” have been conducted; however, several
studies have examined the effect of similar child-rearing policies implemented in
Canada, for which specific target regions were designated. Under the child-rearing
support policy in Canada, daycare space is provided for children under the age offour, at reduced prices; this policy has been in place since 1997, but only in theprovince of Quebec.
Lefebvre and Merrigan [2008] conduct empirical studies to estimate theeffects of this policy on women’s employment by conducting DD analysis, wherein
the province of Quebec was set as the treatment group and the other provinces as
the control group; their analytical results show that the policy had indeed increased
the female labor supply in Quebec. Furthermore, Lefebvre, Merrigan, andVerstraete [2009] conduct detailed analysis of the child-rearing support policy inQuebec and find that the policy had a substantial effect, especially among womenwith low levels of education attainment. Other than these studies, Baker, Gruber,and Milligan [2008] derive similar results, that the child-rearing support policy inQuebec increased the female labor supply there.
Similar to these studies, we undertake a policy evaluation analysis of the“General Childcare-Support for Model-municipalities” by implementing DD
analysis, wherein we set the policy target model regions as the treatment group andthe other regions as the control group.
83. Analytical Approach
3.1 Estimation modelTo estimate the influence of the “model region” designation on women’s
employment, we define the women living in municipalities designated as model
regions under the “General Childcare-Support for Model-municipalities” as thetreatment group, and the women living in the other municipalities as the control
group. To confirm the robustness, we conduct two sets of DD analysis, the one usinga regression and the one using a propensity score matching.
In the DD analysis using a regression model, we estimate the equation (1)as a random-effect probit model, a random-effect linear model, or a fixed-effectlinear model, according to the dependent variables:

𝑌𝑌𝑖𝑖𝑖𝑖 = 𝑀𝑀𝑖𝑖 ∙ 𝑻𝑻𝒕𝒕 𝜷𝜷𝟏𝟏 + 𝛽𝛽2 𝑀𝑀𝑖𝑖 + 𝑻𝑻𝒕𝒕 𝜷𝜷𝟑𝟑 + 𝑿𝑿𝑡𝑡 𝜷𝜷4 + 𝐹𝐹𝑖𝑖 + 𝜀𝜀𝑖𝑖𝑖𝑖 ,(1)

where 𝑌𝑌𝑖𝑖𝑖𝑖 represents dependent variables showing the status of individual 𝑖𝑖 in theyear 𝑡𝑡, with or without employment, regular employment, non-regular employment,

or voluntary non-regular employment, or the average weekly working hours. 𝑀𝑀𝑖𝑖 isa dummy variable indicating a model region (or treatment group); 𝑻𝑻𝒕𝒕 is a vectorof year dummy variables; 𝑿𝑿𝒕𝒕 is a vector of control variables including academic

background, home environment, and other individual attributes; 𝐹𝐹𝑖𝑖 is a time-invariant individual effect; and 𝜀𝜀𝑖𝑖𝑖𝑖 is an error term.

As described above, the “General Childcare-Support for Model-

municipalities” was initiated as a policy in the 2004 fiscal year, based on the action
plan for the first half-term (2005–09). However, municipalities naturally also had
long-term measures in the latter half of the term (2010–14). Therefore, we take into9

account the possibility of a time lag for the effects of the model programs or thechild-rearing support policy of municipalities. Specifically, data from before thepolicy was initiated in 2004 7 and up to 2012 are used to set the time-based
comparison points at three-year intervals: the period 2004-06, 2007-09, and 201012. Thereafter, year dummy variables which take the value of 1 for the period 200709 and that for 2010-12 are included in 𝑇𝑇𝑡𝑡 , by setting the period 2004-06 as a basewhen the policy effect was not evident. Therefore, the coefficient 𝜷𝜷𝟏𝟏 of the cross-terms of the model region dummy and the year dummies represents the averagetreatment effect (ATE) of the child-rearing policy in which this study has an interest.
In the DD analysis using propensity score matching, we match individuals
with similar attributes based on the propensity score. First, we use the probit modelof equation (2), to regress the probability of belonging to the treatment group (i.e.,
propensity score) with individual attributes.

𝑒𝑒𝑖𝑖 = Pr(𝑀𝑀𝑖𝑖 = 1|𝑿𝑿𝑖𝑖 ) = 𝐸𝐸(𝑀𝑀𝑖𝑖 |𝑿𝑿𝒊𝒊 )(2)

In equation (2), 𝑒𝑒𝑖𝑖 indicates the probability of belonging to the treatment group

given individual attributes 𝑿𝑿𝒊𝒊 . Using the estimated propensity score 𝑒𝑒𝑖𝑖 , we derive

the counterfactual dependent variable—if the people living in the model regions(treatment group) lived instead in non-model regions—as follows, via the Kernel
method.

�𝚤𝚤 (0) =𝑌𝑌
7∑𝑙𝑙|𝑀𝑀𝑖𝑖 =0 𝑌𝑌𝑙𝑙 𝐺𝐺((𝑒𝑒𝑙𝑙 − 𝑒𝑒𝑖𝑖 )/ℎ)∑𝑘𝑘|𝑀𝑀𝑖𝑖 =0 𝐺𝐺((𝑒𝑒𝑘𝑘 − 𝑒𝑒𝑖𝑖 )/ℎ)(3)

Because the “child-rearing support comprehensive promotion model municipality program”
started in April 2004, the pre-policy status quo is reflected in the 2004 data of the KHPS—asurvey through which data are captured at the end of January each year.
10In equation (3), we use 𝐺𝐺(∙) for the Kernel function and ℎ for the bandwidthparameter. By collecting from the control group observations whose propensity
score 𝑒𝑒𝑖𝑖 is similar to that of the treatment group, and calculating the weighted

average, we can estimate the counterfactual dependent variable of each worker inthe treatment group. Thereafter, by comparing the difference before and after the
implementation of the “General Childcare-Support for Model-municipalities,” theaverage treatment effect on the treated (ATT) of the child-rearing support policy
is determined by equation (4), based on propensity score matching.

�=
𝐴𝐴𝐴𝐴𝐴𝐴1�𝚤𝚤 (0)�
� �∆𝑌𝑌𝑖𝑖 (1) − ∆𝑌𝑌
𝑁𝑁 𝑇𝑇

(4)

𝑖𝑖|𝑀𝑀𝑖𝑖 =1

In equation (4), 𝑁𝑁 𝑇𝑇 represents the sample size of the treatment group and 𝑌𝑌𝑖𝑖 (1)is the value of the explained valuable of the people living in the model regions (i.e.,
the treatment group).

3.2 Data and variables
In the estimation, we use individual-level panel data obtained from the KHPS,
which is undertaken by the Panel Data Research Center at Keio University. Since
2004, the KHPS has been conducted at the end of each January, and we use datafrom the nine years between 2004 and 2012. Although the KHPS added samples in
FY2007 and FY2010, we use only the samples that were survey targets as of 2004,
in order to identify the changes in individual-level behaviors before and after thepolicy was implemented in 2004. Note that the spouses of the survey targets arealso used as an independent sample to secure the sample size.
To examine the effects of the “General Childcare-Support for Model-

11municipalities,” we only use the sample of married women under the age of 40, 8
considering the fact that the mean delivery age in Japan is approximately 30 years.
As for the dependent variable, we use the dummy variables for employment,
regularemployment,

non-regular

employment

andvoluntary

non-regular

employment as well as the average weekly working hours. The employment dummy
is a binary variable that takes 1 if a worker is employed. Likewise, regular or nonregular employment dummy variables are the one that takes 1 if a worker is
employed as a regular employee or non-regular employee, respectively. The
voluntary non-regular employment dummy takes 1 when the worker voluntarilychooses to work as a non-regular employee. In the KHPS, we can identify whether
the person was forced to work as a non-regular employee since no company offered
her regular employment or she chose to work as a non-regular employee. Thus, we
examine how the policy affected the worker’s choice for the employment status. 9
As for the independent variables for the random-effect probit model or the
probit model to derive the propensity score, we use age, academic background(university/graduate school graduate dummy, junior college/technical college
graduate dummy), annual income of the spouse, living with parents or not (dummy
for living together or the equivalent), and the number of children under the age ofsix, in addition to the model region dummy and year dummies. The state of livingwith the parents or not is classified as follows: the state of living together is applied
to a person who lives with the parents in the same building and makes a living withthem; an equivalent state is applied to a person who lives with the parent(s) in thesame building and makes a living separately from them, or to a person who lives in
8

Although similar analysis is conducted for men, we were not able to confirm any effects of child-rearing support policy.
9

Refer to Yamamoto [2011] for the characteristics of involuntary non-regular employment inJapan.
12

a different building on the same premises where the parents live.
Furthermore, we conduct the estimation including the financial index andthe standard financial scale of each municipality in the independent variables. As
described above, the effects of the “General Childcare-Support for Modelmunicipalities” can be classified as one of two types: (1) the effects of the childrearing support measures undertaken by the municipalities, and (2) the effects ofthe government’s “model region” designation. Between these two types, we assume
that the state of the municipal child-rearing support policy depends greatly on its
financial situation and financial scale, so that the effects of (1) can be identified bycontrolling for these regional elements.
The basic statistics are listed in Table 1. 10 Looking at Table 1, we see thedifferences in individual attributes between the model and non-model regions. These
differences would be controlled for by the individual attributes via the explanatory
variables or propensity score matching.

4. Estimation Results
4.1 Changes in the employment rate in the model and non-model regions
Before conducting DD analysis, we examine the changes in the ratio of theemployment, regular employment, non-regular employment, and voluntary nonregular employment for the model and non-model regions from 2004 to 2012.
Figures 1 (1)–(4) show annual changes in the employment rate of womenin the model regions (treatment group) and those in the non-model regions (control

10

To address the outliers, only the samples within the range of “mean value ± standard deviation

3” are used for the average weekly working hours and the annual income of the spouse.
13

group), between 2004 and 2012. The vertical lines in the figure indicate the 95%
confidence interval, as the difference for each of the two groups can be considered
statistically significant if the vertical lines of the two groups do not intersect.
The employment rate, shown in Figure 1 (1), is lower among the model
regions from 2004 through to around 2007. However, it shows a transition, wherein
it reaches the same level as that seen in the model regions from around 2008; theemployment rate among the model regions then becomes higher, from 2009.
Although the difference seems to be insignificant and other factors are notcontrolled for, we may suppose that the “General Childcare-Support for Modelmunicipalities” could have given rise to prominent policy effects that manifested as
an increase in the employment rate of women from 2010 onward.
A similar tendency is seen for regular employment and non-regular
employment. According to the regular employment rate in Figure 1 (2), thedifference between the model regions and non-model regions from 2010 onward
seems to become smaller, although only slightly. Additionally, according to the nonregular employment rate in Figure 1 (3), this tendency is more prominent, and there
is no difference between the regions from 2004 to 2007; however, the non-regular
employment rate in the model regions increased from 2008 onward at a higher levelof transition, compared to the non-model regions. The same applies to the voluntary
non-regular employment rate in Figure 1 (4), and it is projected that the increasein non-regular employment is not for involuntary reasons.
As confirmed above, it is implied that the policy effects did not appear
immediately; rather they appeared from around 2008, with a time lag. We will take
into account for this possibility when conducting the DD analysis in the following
sections.

144.2 Results of DD analysis based on the regression model

Employment
The results of DD analysis based on the regression model are shown in Tables 2–5.
In the tables, both the coefficient and the marginal effect are reported. In eachtable, the case (1) indicates the estimation result where the financial index andfinancial scale, either of which could affect the attitudes of municipalities vis-à-vis
child-rearing support, are not included in the independent variables, and the case(2) where these factors are included.
Looking at Table 2 (1), we find that the cross-term of the model region
dummy and the year dummy is significantly positive only for non-regular
employment and voluntary non-regular employment of 2010–12. Accordingly, we
can interpret that the employment probability for non-regular employment,
especially voluntary non-regular employment, in the target model regions wasincreased by the model programs. However, looking at the marginal effect, it isshown that the change is positive but not statistically significant, implying that themagnitude of the effect on non-regular employment and voluntary non-regular
employment were not so large. Additionally, we could find that the cross-term withthe 2007–09 dummy for regular employment is significantly negative for both thecoefficient and marginal effect. Thus, we could understand that that the policy may
have caused a decrease in the regular employment rate although the marginal effectis extremely small.
On the other hand, according to Table 2 (2) in which we control forregional factors such as financial index and financial scale, the cross-term of themodel region dummy and year dummy is not significantly positive for both thecoefficient and marginal effect whereas the financial index is significantly positive.
15

That is, after controlling for the regional factors, the significant policy effects on
non-regular employment and voluntary non-regular employment from the 2010–12
period shown in Table 2 (1) disappears.
From this result, we can interpret that the effects seen in this period ofchild-rearing support policy stem mainly from the child-rearing support measures
implemented and enhanced by the municipalities under the “Act for Measures to
Support the Development of the Next Generation,” rather than by the designationof these municipalities as “model regions.”

Employment across individual attributes
Next, to examine the possibility that the child-rearing support policy has effects on
women who bear specific attributes, the estimation is performed by taking the crossterm—which multiplies the model region dummy, the year dummy, and the dummy
variables for the academic background or for the number of children. The estimation
results are shown in Tables 3 and 4.
Looking at Table 3 (1) examining the differences in policy effects byacademic background, the cross-term with the junior/technical college graduate
dummy for the 2010–12 period of non-regular employment is significantly positive.
Furthermore, the cross-term with the junior/technical college graduate dummy
from the 2010–12 period of voluntary non-regular employment is significant for thecoefficient. Thus, we could point out that married women who are junior/technicalcollege graduates and living in the model regions had a higher probability of being
employed as non-regular workers, in line with their wishes, following the policy
implementation.
We can also confirm these results in Table 3 (2), in which regional factors
such as the financial index are controlled for, implying that the positive effects on
16

non-regular employment among junior/technical college graduate married womenare caused not only by child-rearing support measures at the municipal level, butalso by the government’s “model region” designation.
In Table 4 (1), we can find that the cross-term with the dummy for having
more than two children is significantly positive for the employment, non-regular
employment, and voluntary non-regular employment of the 2010–12 period.
Considering the fact that the cross-term with the dummy for having one child is
not significant, we can say that a woman with more children tend to experience theeffects of child-rearing support policy. However, in Table 4 (2) where we control forregional factors, these significant policy effects are not seen, implying that the childrearing support policy for married women with more children was effective due tothe measures undertaken by the municipalities, rather than the government’s model
region program.

Hours of work
Table 5 shows the estimation results of the effects of child-rearing support
policy on the average weekly working hours of women, by using fixed-effect andrandom-effect models. In these estimations, we put zero in the weekly working hours
for the unemployed. As shown in the results of the Hausman test in the bottom
row of Table 5, the fixed-effect model is adopted for the case using the whole sample,
while the random-effect model is for other cases.
Looking at Table 5 (1), no significant coefficients are found for the crossterm of the model region dummy and year dummy for the case using the wholesample and the sample of being employed, in the model supported by the Hausman
test. However, in the case using the sample of regular employment, positive andsignificant policy effects are obtained for the 2010–12 period in the random-effect17

model, which is supported by the Hausman test. As shown in Table 5 (2), theseresults do not change, even if the regional factors are controlled for. In other words,
we can interpret our findings as the “General Childcare-Support for Modelmunicipalities” reduced the burden of childcare for women employed as regular
workers, and those women could increase their hours of work.

4.3 Results of DD analysis based on propensity score matching
To confirm the robustness of the results of DD analysis based on the regression
model, we show the results of propensity score matching in Table 6.
In Table 6, the employment rate, regular employment rate, non-regular
employment rate, and voluntary non-regular employment rate are shown in rows(a) and (b) for the model regions and non-model regions within the period of 2004–
06, 2007–09, and 2010–12. The “Difference (a) – (b)” is the difference between each
employment rate in model regions and non-model ones. Among these differences,“Nonmatching” is the simple difference, and “Matching” is the difference derived
through the propensity score matching. In addition, “Difference-in-Differences” isthe ATT from the nonmatching and matching methods, determined by thedifference from the period 2004–06. If the child-rearing support policy is effective,
this ATT should be significantly positive.

Without controlling for regional factors
In the same manner as the DD analysis based on the regression model,
Table 6 shows the following cases: (1) without controlling for regional factors, and(2) with controlling for regional factors. Whether or not the regional factors arecontrolled refers to whether or not the financial index and financial scale areincluded in the explanatory variable 𝑿𝑿𝒊𝒊 of the probit model in equation (2) to18

calculate the propensity score. Therefore, we can interpret that the ATT in
Table 6 (1) incorporates the total effects of the child-rearing support policy
introduced by the municipalities and of the government’s designation to the “model
region,” and that the ATT in Table 6 (2) reflects the effects of the government’s
designation.
Looking at the “Difference” in Table 6 (1), we can see that the difference
for the employment rate is significantly negative for each of the 2004–06 and 2007–
09 periods, for both matching and nonmatching. This means that at the start ofthe policy implementation, the employment rate for women was significantly lower
in the model regions than in the non-model regions. And, this result does not change,even when the attributes are controlled through propensity score matching.
In addition, we can see that the regular employment rate for 2007–09 andeach of the non-regular employment rate and voluntary non-regular employment
rate for 2004–06 are also significantly lower in the model regions, even when theattributes are controlled through propensity score matching. However, the negative
significance of the non-regular employment rate and the voluntary non-regular
employment rate is not observed during the model program period or in subsequent
periods. Furthermore, the results of matching estimation for 2010–12 show that therates in the model regions are significantly higher.
These tendencies are also shown in the “Difference-in-Differences” results.
Specifically, for the non-regular employment rate and voluntary non-regular
employment rate of 2007–09 and 2010–12, the ATT is significantly positive, bothfor matching and nonmatching. Furthermore, the ATT through propensity scorematching for the employment rate is significantly positive for the 2010–12 period.
According to these results, the “General Childcare-Support for Modelmunicipalities” can be interpreted as having helped improve the non-regular19

employment rate and voluntary non-regular employment rate—both of which werelower in the model regions than in the non-model regions—and increasing theprobability of non-regular employment among women.

With controlling for regional factors
Next, we focus on the “Difference” in Table 6 (2), in which the regional factors arecontrolled for. First, the negative significance of the employment rate, regular
employment rate, non-regular employment rate, and voluntary non-regular
employment rate, which was shown in Table 6 (1), is not seen in Table 6 (2).
Instead, we can occasionally find significantly positive differences. Therefore, we
can infer that the lower employment rates in the model regions compared to thenon-model regions—all of which are observed prior to the policy implementation—
are caused by the regional factors such as financial index and financial scale, andthat the employment environment prior to policy implementation was better in themodel regions, according to a comparison of municipalities with similar regional
factors.
Additionally, focusing on the “Difference-in-Differences” results, we findthat the significantly positive ATTs for the employment rate and non-regular
employment rate shown in Table 6 (1) are not estimated in Table 6 (2). Thus, we
can determine that the effects of the child-rearing policy shown in Table 6 (1) werecaused not by the government’s “model region” designation but mainly by themeasures taken by the municipalities.

205. Concluding remarks
To promote the utilization of women in the labor market, developing anenvironment in which women can both work and take care of their children is highly
important in Japan. However, no particular consensus has been obtained vis-à-vis
the influence of child-rearing support policy undertaken by the government or themunicipalities on the employment of women. In this paper, we estimate the effectof the local government’s child-rearing support policies implemented in Japan in
the 2000s, the “General Childcare-Support for Model-municipalities.” We apply thestandard method of the policy evaluation, regression and propensity score matching
DD analysis, to derive the effect of the policy.
The main results we obtained can be summarized as follows. First, the“General Childcare-Support for Model-municipalities” increased the non-regular
employment of women in the target model regions (municipalities)—especiallyvoluntary non-regular employment. This tendency was more evident among women
who are junior/technical college graduates and women with more children under
the age of six. We also confirmed the tendency that the program increased theworking hours of women who work as regular employees. On the other hand, we
find that many of these policy effects disappear after controlling for the regional
factors such as financial index and financial scale. This result implies that the effectsof the program on women’s employment may depend more on the child-rearing
support measures of the municipalities than on the government’s “model region”
designation.
Considering these results, we could evaluate the “General ChildcareSupport for Model-municipalities” as follows. First, focusing on the increase in nonregular employment—mainly in voluntary non-regular employment, we can say that21

the “General Childcare-Support for Model-municipalities” has given rise to certain
effects. If we observe the increase in non-regular employment through the
involuntary non-regular employment, we should conclude that the child-rearing
policy did not improve female labor market condition so that it increased theprobability to find regular employment. However, our results indicate that thepolicy has supported women who want to work as non-regular workers, and thus
the policy can be interpreted as having improved the employment environment for
married women while parenting young children.
Next, the fact that the program has increased the working hours of married
women who are employed as regular workers can be interpreted as follows. Generally
speaking, in Japan, it is more difficult for women to be employed as regular workers
than as non-regular workers. Thus, even if the childcare burden were reduced by
the policy, it is not easy for married women to be employed as regular workers whileparenting young children. In fact, we found that the child-rearing policy did notincrease the regular employment rate among women. Instead of increasing theregular employment, the policy may have helped women who were working as
regular workers prior to policy implementation reduce their childcare burden, thus
allow them to spend more time working than would otherwise have been the case.
In line with the aforementioned results, we can conclude that the “GeneralChildcare-Support for Model-municipalities” or the proactive child-rearing support
measures by the municipalities under the “Act for Measures to Support theDevelopment of the Next Generation” have had certain effects on women’s
employment. However, we also find that many of these effects have been caused notby the government’s “model region” designation, but by measures taken at themunicipal level. Therefore, we can point out that further investigation should beneeded for the government’s model program in target regions.
22

Finally, we wish to address reservation and limitations in this study.
Although we have examined the total effects of the “General Childcare-Support for
Model-municipalities,” we did not examine in detail which programs are effective.
As we described in Section 2, various child-rearing support programs included in
the mandatory or optional programs have been implemented in the model regions.
Due to the data limitation, however, we were not able to conduct detailed analysis
to examine the effectiveness for various programs. This issue will be addressed infuture research.
Next, this study addresses the short to middle-term effects of the “GeneralChildcare-Support for Model-municipalities,” but it does not examine long-term
effects. Although our results indicate policy effects vis-à-vis increases in theparticipation of women in non-regular employment and increases in the working
hours of women who are employed as regular workers, it is possible that in thelonger term, effects vis-à-vis increases in the probability of employment as a regular
worker on account of reduced childcare burden could become more prominent.
Particularly, as municipalities implemented long-term measures between 2005 and2014 under the “Act for Measures to Support the Development of the Next
Generation,” we can say that additional analysis that features an expanded target
period is required.

23

References
Michael Baker, Jonathan Gruber and Kevin Milligan [2008] “Universal Child Care,
Maternal Labor Supply, and Family Well-Being” Journal of Political
Economy, 2008, vol. 116, issue 4, pages 709-745.
Pierre Lefebvre and Philip Merrigan [2008] “Child-Care Policy and the Labor
Supply of Mothers with Young Children: A Natural Experiment from
Canada” Journal of Labor Economics, 2008, vol. 26, issue 3, pages 519-548.
Pierre Lefebvre, Philip Merrigan and Matthieu Verstraete [2009] “Dynamic labor
supply effects of childcare subsidies: Evidence from a Canadian naturalexperiment on low-fee universal child care” Labour Economics, 2009, vol. 16,
issue 5, pages 490-502.
Akiko Ohishi [2003] “The influence of fee for day care on maternal employment”
Quarterly Journal of Social Security Research, vol.39, no.1, pages 55-69. (inJapanese)
Akira Kawaguchi [2011] “The influence of long-term employment system and work–
life balance measures on active participation by women” Report of theresearch on the relation between realization of work-life balance society andproductivity, Economic and Social Research Institute, Cabinet Office, pages
81-96. (in Japanese)
Terukazu Suruga and Jianhua Zhang [2003] “Influence of parental leave programson childbirths and continued employment of women: Quantitative analysis
by panel data” Japanese Journal of Research on Household Economics, no.59,
pages 56-63. (in Japanese)
Yasunobu Tomita [1994] “The effect of parental leave and work schedule on
women’s retention after pregnancy” The Economic Review, vol.39, no.2,
pages 43-56. (in Japanese)24

Yoshio Higuchi [1994] “Empirical analysis of the parental leave,” Modern family
and social security, National Institute of Population and Social Security
Research ed., University of Tokyo Press, pages 181-204. (in Japanese)
Yoshio Higuchi, Toshiyuki Matsuura and Kazuma Sato [2007] “Impact of Regional
Factors on Births and Wives’ Continuation in Employment: Panel survey ofconsumers by the Institute for Research on Household Economics”, RIETI
Discussion Paper Series, 07-J-012. (in Japanese)
Hisakazu Matsushige and Mamiko Takeuchi [2008] “Effects of Intra-corporate
Policies on the Work of Female Employees” Osaka School of International
Public Policy Discussion Paper 13(1), pages 252-271. (in Japanese)
Katsura Maruyama [2001] “Practical Use of Working Women and the Analysis of
Their Working Style after Childbirth” Journal of Population Problems,
vol.57, no.2, pages 3-18. (in Japanese)
Yoko Morita and Yoshihiro Kaneko [1998] “The Effects of the Child Care Leave on
Women in the Workforce” Journal of the Japan Institute of Labour, No.459,
pages 50-60. (in Japanese)
Isamu Yamamoto [2011] “Involuntary Non-regular Workers in Japan and Their
Mental Health”, Non-regular Employment System Reform in Japan:
Changing the way people work, Kotaro Tsuru, Yoshio Higuchi and Yuichiro
Mizumachi eds., Nippon Hyoron Sha Co., Ltd., Tokyo, Japan, Chapter 4,
pages 93-120. (in Japanese)
Isamu Yamamoto [2014] “Workplace Environment and Female Employment: An
empirical analysis using firm panel data” RIETI Discussion Paper Series, 14J-017. (in Japanese)25

Table 1

Basic statistics

Variable
Employment dummy
Regular employment dummy
Nonregular employment dummy
Voluntary nonregular employment dummy
Average weekly working hours
Age
University/Graduate school graduate dummy
Junior/Technical college graduate dummy
Annual income of the spouse (unit: 1,000 yen)
Living together dummy
Equivalent to the state of living together dummy
Number of children under six
With one child dummy
With more than two children dummy
Financial index
Standard financial scale (unit: 1,000 yen)
Obervations

Women
Model regions Nonmodel regions
0.38
0.46
(0.48)(0.50)
0.11
0.16(0.31)(0.36)
0.26
0.29(0.44)(0.45)
0.25
0.27(0.44)(0.44)
10.99
14.15(16.17)(18.11)33.78
33.75(3.97)(3.94)
0.19
0.13(0.40)(0.34)
0.29
0.26(0.45)(0.44)
4824.524675.04(1904.40)(1941.71)
0.05
0.11(0.22)(0.31)
0.10
0.11(0.31)(0.31)
0.97
0.84(0.82)(0.84)
0.40
0.37(0.49)(0.48)
0.27
0.22(0.45)(0.41)
0.88
0.80(0.22)(0.23)
75700.00
44000.00(34900.00)(38300.00)
682
3858

Note: The numbers in parentheses are standard deviations.

26

Table 2

Estimation results for employment(1) Without controlling for regional factors
Employment
Marginal
effectModel regions dummy-0.0388-0.0148× 2007-09 dummy(0.229)(0.0864)
Model regions dummy
0.511
0.201
× 2010-12 dummy(0.314)(0.123)Model regions dummy-0.191-0.0712(0.222)(0.0808)
2007-09 dummy
0.317***
0.123***(0.0916)(0.0360)
2010-12 dummy
0.132
0.0509(0.114)(0.0444)Age
0.0261*0.00997*(0.0150)(0.00575)
University/Graduate school
0.304
0.119
graduate dummy(0.232)(0.0920)
Junior /Technical college
0.108
0.0416
graduate dummy(0.160)(0.0617)
Annual income of the spouse -0.000127***-4.85e-05***(2.88e-05) (1.10e-05)
Living together dummy
0.337*
0.132*(0.187)(0.0741)
Equivalent to the state of
0.634***
0.248***
living together dummy(0.166)(0.0636)
Number of children under six-0.617*** -0.236***(0.0681)(0.0255)
Constant term
-0.316(0.503)Observations
Coefficient

Regular employment

Nonregular employment

Marginal
Marginal
Coefficienteffecteffect-0.905** -1.86e-07**
0.288
0.0708(0.444)(8.58e-08)(0.218)(0.0606)-0.0390-2.84e-08
0.584**
0.163(0.452)(2.99e-07)(0.292)(0.0996)
0.375
6.69e-07-0.149-0.0302(0.271)(9.34e-07)(0.196)(0.0374)
0.08997.96e-080.243***
0.0550**(0.162)(1.69e-07)(0.0879)(0.0217)0.09578.98e-08
0.03020.00654(0.186)(2.16e-07)(0.112)(0.0246)-0.0843*** -6.73e-08*
0.0555*** 0.0119***(0.0231)(3.50e-08)(0.0150)(0.00324)
1.536***
7.22e-05-0.691*** -0.111***(0.345)(9.97e-05)(0.209)(0.0246)
0.693***
1.90e-06-0.186-0.0381(0.170)(1.51e-06)(0.146)(0.0283)-7.01e-05* -5.60e-11-9.09e-05*** -1.95e-05***(3.94e-05)(0)(2.83e-05) (6.25e-06)
0.260
3.76e-07
0.03060.00665(0.247)(6.25e-07)(0.182)(0.0402)0.226
2.97e-07
0.574***
0.154***(0.221)(4.58e-07)(0.153)(0.0489)-0.529*** -4.22e-07**-0.458*** -0.0982***(0.124)(1.77e-07)(0.0644)(0.0149)-2.001**-2.162***(0.780)(0.515)4149
Coefficient

Voluntary nonregular
employment
Marginal
Coefficienteffect0.310
0.0737(0.212)(0.0576)
0.561**
0.150(0.284)(0.0933)-0.116-0.0225(0.185)(0.0342)0.203**
0.0435**(0.0843)(0.0195)
0.01360.00278(0.112)(0.0231)0.0543*** 0.0111***(0.0143)(0.00295)-0.736*** -0.109***(0.202)(0.0216)-0.194-0.0376(0.140)(0.0257)-6.31e-05** -1.29e-05**(2.72e-05) (5.65e-06)
0.04630.00964(0.179)(0.0380)0.650***
0.172***(0.148)(0.0476)-0.403*** -0.0821***(0.0616)(0.0134)-2.336***(0.495)

Note: 1) The numbers in parentheses are robust standard errors.
2) *, **, and *** indicate statistical significance at the 10, 5, and 1% levels, respectively.

27

(2) With controlling for regional factors
Employment
Marginal
effect0.223
0.0870(0.294)(0.117)0.498
0.196(0.449)(0.176)0.205
0.0797(0.293)(0.115)0.187
0.0720(0.129)(0.0503)
0.203
0.0785(0.195)(0.0766)
0.01900.00725(0.0202)(0.00771)
0.101
0.0389(0.308)(0.120)0.08010.0307(0.198)(0.0762)-9.98e-05** -3.80e-05**(4.37e-05) (1.66e-05)
0.315
0.123
(0.205)(0.0812)0.766***
0.298***(0.212)(0.0786)-0.667*** -0.254***(0.0983)(0.0368)-0.250-0.0951(0.372)(0.142)-0.165-0.0629(0.107)(0.0409)
2.878(1.919)Coefficient

Model regions dummy× 2007-09 dummy
Model regions dummy× 2010-12 dummy
Model regions dummy
2007-09 dummy
2010-12 dummy
Age
University/Graduate school
graduate dummy
Junior /Technical college
graduate dummy
Annual income of the spouse
Living together dummy
Equivalent to the state ofliving together dummy
Number of children under six
Financial index
ln standard financial scale
Constant term
Observations

Regular employment

Nonregular employment

Marginal
Coefficienteffect-0.784-1.47e-07
0.434(0.594)(1.56e-06)(0.293)1.088*
1.95e-05
0.266(0.602)(0.000169)(0.434)-0.0523-3.07e-08
0.191(0.343)(3.37e-07)(0.277)-0.0202-1.28e-08
0.121(0.242)(2.30e-07)(0.125)-0.188-9.33e-08
0.208(0.327)(1.01e-06)(0.176)-0.0716**-4.61e-08
0.0473**(0.0314)(4.74e-07)(0.0187)
1.183***
1.86e-05-0.619**(0.385)(0.000156)(0.278)1.053***
5.28e-06-0.346*(0.265)(4.75e-05)(0.184)-0.000119** -7.69e-11-6.47e-05(5.90e-05) (8.01e-10)(4.04e-05)-0.0990-5.22e-08
0.165(0.359)(5.12e-07)(0.195)0.329
4.53e-07
0.638***(0.255)(4.68e-06)(0.187)-0.445***-2.87e-07-0.537***(0.171)(2.99e-06)(0.0897)-2.006***-1.29e-06
0.634*(0.535)(1.34e-05)(0.331)-0.227-1.46e-07-0.0983(0.153)(1.50e-06)(0.101)3.359-0.718(2.540)(1.764)2279
Coefficient

Marginal
effect0.121(0.0943)0.0697(0.127)0.0473(0.0729)0.0286(0.0305)0.0515(0.0469)
0.0110**(0.00437)-0.110***(0.0367)-0.0740**(0.0359)-1.50e-05(9.43e-06)
0.0410(0.0516)0.185***(0.0640)-0.124***(0.0213)0.147*(0.0774)-0.0228(0.0234)

Voluntary nonregular
employment
Marginal
Coefficienteffect0.456
0.123(0.281)(0.0888)0.279
0.0702(0.419)(0.119)0.195
0.0461(0.260)(0.0656)0.08060.0181(0.120)(0.0275)
0.143
0.0331(0.174)(0.0424)0.0497*** 0.0110***(0.0181)(0.00405)-0.589**-0.100***(0.264)(0.0336)-0.363**-0.0733**(0.178)(0.0326)-3.95e-05-8.71e-06(3.86e-05) (8.55e-06)
0.182
0.0433(0.200)(0.0514)0.721***
0.206***(0.182)(0.0621)-0.464*** -0.102***(0.0846)(0.0191)
0.592*
0.131*(0.311)(0.0691)-0.110-0.0242(0.0967)(0.0214)-0.782(1.696)

Note: 1) The numbers in parentheses are robust standard errors.
2) *, **, and *** indicate statistical significance at the 10, 5, and 1% levels, respectively.

28

Table3

Estimation results for employment

across the academic background(1) Without controlling for regional factors
Employment
Marginal
effectModel regions dummy × 2007-09 dummy-0.409-0.143× University/Graduate school graduate dummy(0.741)(0.232)Model regions dummy × 2007-09 dummy-0.332-0.119× Junior/Technical college graduate dummy(0.439)(0.145)Model regions dummy × 2010-12 dummy-0.149-0.0555
× University/Graduate school graduate dummy(0.831)(0.301)Model regions dummy × 2010-12 dummy
0.816
0.315
× Junior/Technical college graduate dummy(0.608)(0.213)Model regions dummy
0.118
0.0457× 2007-09 dummy(0.280)(0.110)Model regions dummy
0.289
0.114
× 2010-12 dummy(0.461)(0.184)Model regions dummy-0.192-0.0717(0.227)(0.0824)
2007-09 dummy
0.318***
0.123***(0.0917)(0.0360)
2010-12 dummy
0.132
0.0509(0.114)(0.0444)Age
0.0260*0.00994*(0.0151)(0.00576)
University/Graduate school
0.328
0.128
graduate dummy(0.236)(0.0934)
Junior/Technical college
0.101
0.0387
graduate dummy(0.161)(0.0624)
Annual income of the spouse-0.000126*** -4.81e-05***(2.87e-05)(1.10e-05)
Living together dummy
0.334*
0.131*(0.188)(0.0745)
Equivalent to the state of
0.638***
0.250***
living together dummy(0.166)(0.0636)
Number of children under six-0.618***-0.236***(0.0681)(0.0255)
Constant term
-0.320(0.504)4149
Observations
Coefficient

Regular employment
Coefficient

Marginal
effect-3.129*** -1.05e-07**(0.586)(4.91e-08)-2.006-8.85e-08**(1.955)(4.15e-08)-1.981*** -9.13e-08**(0.711)(4.31e-08)
0.03091.50e-08(0.402)(2.09e-07)
0.701
2.58e-06(0.628)(7.74e-06)
0.226
1.64e-07(0.250)(2.75e-07)
0.07693.79e-08(0.163)(9.32e-08)
0.07773.99e-08(0.188)(1.15e-07)-0.0838*** -3.77e-08*(0.0238)(2.07e-08)
1.640***
6.96e-05(0.360)(0.000101)
0.779***
1.51e-06(0.173)(1.23e-06)-6.59e-05*-0(4.00e-05)
(0)
0.269
2.27e-07(0.247)(3.75e-07)
0.182
1.23e-07(0.221)(2.18e-07)-0.556*** -2.50e-07**(0.129)(1.09e-07)-2.112***(0.796)
4116

Nonregular employment
Marginal
effect0.120
0.0273(0.671)(0.163)0.122
0.0280(0.463)(0.113)0.295
0.0736(0.845)(0.240)1.119**
0.368*(0.547)(0.217)0.237
0.0569(0.256)(0.0681)
0.169
0.0396(0.410)(0.104)-0.162-0.0325(0.198)(0.0373)0.244***
0.0552**(0.0879)(0.0217)0.03180.00690(0.112)(0.0246)
0.0549*** 0.0118***(0.0150)(0.00324)-0.699***-0.111***(0.213)(0.0249)-0.220-0.0445(0.148)(0.0283)-9.04e-05*** -1.94e-05***(2.82e-05)(6.21e-06)
0.02240.00486(0.183)(0.0400)
0.584***
0.157***(0.153)(0.0490)-0.456*** -0.0978***(0.0644)(0.0148)-2.137***(0.517)4149
Coefficient

Voluntary nonregular
employment
Marginal
Coefficienteffect-0.0375-0.00747(0.562)(0.110)0.138
0.0304(0.454)(0.107)0.332
0.0808(0.813)(0.230)0.971*
0.301(0.524)(0.205)0.274
0.0640(0.251)(0.0661)0.186
0.0419(0.397)(0.0978)-0.124-0.0240(0.187)(0.0344)0.204**
0.0436**(0.0844)(0.0195)
0.01510.00309(0.112)(0.0231)0.0538*** 0.0110***(0.0144)(0.00295)-0.740***-0.109***(0.205)(0.0219)-0.225-0.0431*(0.143)(0.0257)-6.25e-05** -1.27e-05**(2.71e-05)(5.62e-06)
0.03920.00812(0.179)(0.0378)
0.659***
0.175***(0.148)(0.0477)-0.401*** -0.0817***(0.0615)(0.0133)-2.315***(0.496)4149

Note: 1) The numbers in parentheses are robust standard errors.
2) *, **, and *** indicate statistical significance at the 10, 5, and 1% levels, respectively.

29

(2) With controlling for regional factors
Employment
Marginal
CoefficienteffectModel regions dummy × 2007-09 dummy
0.08510.0328(0.397)(1.019)× University/Graduate school graduate dummy-0.0353-0.0941
Model regions dummy × 2007-09 dummy(0.171)(0.465)× Junior/Technical college graduate dummy
0.653
0.256
Model regions dummy × 2010-12 dummy(0.445)(1.182)× University/Graduate school graduate dummy
0.555***
1.781**
Model regions dummy × 2010-12 dummy(0.771)(0.111)× Junior/Technical college graduate dummy
0.234
0.0913Model regions dummy(0.355)(0.141)× 2007-09 dummy-0.175-0.0647
Model regions dummy(0.553)(0.197)× 2010-12 dummy
0.204
0.0789Model regions dummy(0.295)(0.116)0.191
0.0734
2007-09 dummy(0.0506)(0.130)0.205
0.0791
2010-12 dummy(0.197)(0.0772)
0.00697
0.0183Age(0.0205)(0.00779)
0.08690.0333
University/Graduate school(0.316)(0.122)
graduate dummy
0.03240.0124
Junior/Technical college(0.0784)(0.205)
graduate dummy-9.93e-05** -3.78e-05**
Annual income of the spouse(4.38e-05)(1.67e-05)
0.312
0.122
Living together dummy(0.206)(0.0816)
0.793***
0.308***
Equivalent to the state of(0.213)(0.0784)
living together dummy-0.672***-0.256***
Number of children under six(0.0997)(0.0373)-0.294-0.112Financial index(0.142)(0.373)-0.165-0.0628ln standard financial scale(0.109)(0.0414)2.943
Constant term
(1.941)
Observations
2279

Regular employment

Nonregular employment

Marginal
effectMarginal
effect
0.0563(0.272)0.0771(0.142)0.506(0.419)0.781***(0.107)0.0776(0.0977)-0.116***(0.0377)0.0471(0.0730)
0.0295(0.0303)
0.0526(0.0469)0.0105**(0.00435)-0.114***(0.0353)-0.0911***(0.0353)-1.53e-05(9.33e-06)
0.0372(0.0502)
0.199***(0.0646)-0.124***(0.0214)0.140*(0.0775)-0.0222(0.0233)

Coefficient-2.104***(0.813)-1.80e-07(1.77e-06)-2.657***-1.69e-07(0.806)(1.67e-06)-0.0401-2.83e-08(0.702)(4.86e-07)
0.00183
2.286***(0.689)(0.0107)-0.0505-3.57e-08(0.335)(3.64e-07)-0.0233-1.77e-08(0.239)(2.77e-07)-1.15e-07-0.195(0.327)(1.14e-06)-0.0702**-5.43e-08(5.11e-07)(0.0313)
3.96e-05
1.328***(0.387)(0.000299)
9.32e-06
1.181***(0.267)(7.56e-05)-0.000113* -8.72e-11(5.88e-05)(8.36e-10)-0.0687-4.62e-08(0.359)(4.23e-07)
0.276
3.98e-07(0.264)(3.81e-06)-0.450**-3.48e-07(0.176)(3.32e-06)-1.59e-06-2.053***(0.538)(1.51e-05)-0.228-1.76e-07(0.153)(1.66e-06)
3.325(2.580)2252

Coefficient

0.222(0.971)
0.295(0.482)1.450(1.129)2.486***(0.703)0.299(0.336)-0.795*(0.442)0.194(0.282)0.127(0.126)0.216(0.179)0.0461**(0.0191)-0.675**(0.287)-0.447**(0.195)-6.75e-05*(4.08e-05)
0.154(0.195)0.689***(0.187)-0.545***(0.0906)
0.616*(0.338)-0.0977(0.102)-0.648(1.795)2279

Voluntary nonregular
employment
Marginal
Coefficienteffect-0.0664-0.0138(0.775)(0.156)
0.06890.277(0.472)(0.132)1.373
0.470(1.062)(0.410)2.152***
0.717***(0.675)(0.159)0.368
0.0942(0.327)(0.0962)-0.681-0.100**(0.427)(0.0394)
0.203
0.0473(0.265)(0.0662)0.08640.0190(0.121)(0.0273)
0.151
0.0343(0.0424)(0.176)
0.0486*** 0.0105***(0.0184)(0.00403)-0.631**-0.103***(0.272)(0.0328)-0.453**-0.0875***(0.0323)(0.188)-9.00e-06-4.16e-05(3.90e-05)(8.47e-06)
0.04020.172(0.201)(0.0503)0.770***
0.220***(0.182)(0.0626)-0.101***-0.469***(0.0850)(0.0191)
0.581*
0.126*(0.316)(0.0692)-0.0235-0.109(0.0984)(0.0214)-0.732(1.727)2279

Note: 1) The numbers in parentheses are robust standard errors.
2) *, **, and *** indicate statistical significance at the 10, 5, and 1% levels, respectively.

30

Table4

Estimation results for employment
across the number of children(1) Without controlling for regional factors
Employment
Marginal
effect0.467
0.184(0.395)(0.155)-0.0851-0.232(0.160)(0.458)0.164
0.0640(0.483)(0.191)
1.179**
0.428***(0.541)(0.147)-0.201-0.0744(0.124)(0.348)0.07000.179(0.350)(0.138)-0.0668-0.179(0.218)(0.0797)0.319***
0.123***(0.0360)(0.0915)0.128
0.0496(0.0444)(0.114)0.008930.0233(0.00575)(0.0150)
0.135
0.345
(0.231)(0.0916)0.125
0.0480(0.159)(0.0615)-0.000134*** -5.12e-05***(2.89e-05)(1.11e-05)
0.152**
0.388**(0.187)(0.0739)0.255***
0.651***(0.163)(0.0620)-0.912***-0.325***(0.118)(0.0378)-1.319***-0.409***(0.148)(0.0342)-0.0968(0.503)
Coefficient

Model regions dummy × 2007-09 dummy
× With one child dummy
Model regions dummy × 2007-09 dummy
× With more than two children dummy
Model regions dummy × 2010-12 dummy
× With one child dummy
Model regions dummy × 2010-12 dummy
× With more than two children dummy
Model regions dummy× 2007-09 dummy
Model regions dummy× 2010-12 dummy
Model regions dummy
2007-09 dummy
2010-12 dummy
Age
University/Graduate school
graduate dummy
Junior/Technical college
graduate dummy
Annual income of the spouse
Living together dummy
Equivalent to the state ofliving together dummy
With one child dummy
With more than two children dummy
Constant term

Regular employment
Coefficient
0.333(0.879)-0.453(0.876)0.474(0.816)-0.381(0.791)-0.936(0.748)-0.0941(0.508)0.351(0.274)0.114(0.158)0.107(0.188)-0.0854***(0.0233)
1.568***(0.358)0.707***(0.173)-7.18e-05*(4.04e-05)
0.273(0.252)0.266(0.216)-0.896***(0.185)-1.032***(0.245)-1.851**(0.782)

Observations

Nonregular employment
Marginal
effect0.118
0.0269(0.0861)(0.356)0.04260.00935(0.108)(0.480)0.149
0.0345(0.456)(0.114)
1.213**
0.405*(0.220)(0.557)0.04690.199(0.304)(0.0783)0.217
0.0518(0.0892)(0.338)-0.117-0.0239(0.193)(0.0378)
0.0545**0.241***(0.0883)(0.0218)
0.02370.00512(0.112)(0.0244)0.0533*** 0.0114***(0.00323)(0.0150)-0.661***-0.107***(0.208)(0.0250)-0.174-0.0357(0.145)(0.0284)-9.56e-05*** -2.05e-05***(2.84e-05)(6.27e-06)
0.06200.0137(0.183)(0.0413)0.582***
0.156***(0.0489)(0.152)-0.613***-0.121***(0.109)(0.0213)-1.014***-0.160***(0.140)(0.0203)-2.015***(0.517)4149

Marginal
effect6.99e-07(3.67e-06)-1.51e-07(1.06e-07)
1.57e-06(6.73e-06)-1.42e-07(1.17e-07)-1.99e-07**(9.43e-08)-6.40e-08(2.74e-07)
6.25e-07(9.03e-07)
1.11e-07(1.90e-07)
1.09e-07(2.40e-07)-7.23e-08*(3.76e-08)
8.46e-05(0.000120)
2.11e-06(1.69e-06)-6.08e-11(0)4.32e-07(7.12e-07)
4.10e-07(5.68e-07)-8.85e-07**(4.33e-07)-5.47e-07**(2.46e-07)

Coefficient

Voluntary nonregular
employment
Marginal
Coefficienteffect0.003780.0184(0.345)(0.0717)-0.0297-0.00594(0.0935)(0.475)-0.00910-0.0459(0.442)(0.0853)
1.074**
0.342(0.548)(0.217)0.285
0.0667(0.300)(0.0797)
0.299
0.0709(0.331)(0.0900)-0.0876-0.0172(0.183)(0.0347)0.201**
0.0429**(0.0195)(0.0848)0.00999
0.00204(0.112)(0.0230)
0.0524*** 0.0107***(0.00294)(0.0144)-0.710***-0.106***(0.201)(0.0219)-0.185-0.0357(0.140)(0.0257)-6.70e-05** -1.36e-05**(2.73e-05)(5.66e-06)
0.07250.0153(0.179)(0.0389)
0.174***
0.656***(0.148)(0.0476)-0.518*** -0.0980***(0.105)(0.0196)-0.897***-0.138***(0.136)(0.0188)-2.216***(0.498)(0.0189)

Note: 1) The numbers in parentheses are robust standard errors.
2) *, **, and *** indicate statistical significance at the 10, 5, and 1% levels, respectively.

31

(2) With controlling for regional factors
Employment

Model regions dummy × 2007-09 dummy
× With one child dummy
Model regions dummy × 2007-09 dummy
× With more than two children dummy
Model regions dummy × 2010-12 dummy
× With one child dummy
Model regions dummy × 2010-12 dummy
× With more than two children dummy
Model regions dummy× 2007-09 dummy
Model regions dummy× 2010-12 dummy
Model regions dummy
2007-09 dummy
2010-12 dummy
Age
University/Graduate school
graduate dummy
Junior/Technical college
graduate dummy
Annual income of the spouse
Living together dummy
Equivalent to the state ofliving together dummy
With one child dummy
With more than two children dummy
Financial index
ln standard financial scale
Constant term
Observations

Marginal
Coefficienteffect-0.372-0.132(0.155)(0.481)-1.353***-0.338***(0.0625)(0.522)-0.0211-0.0559(0.229)(0.612)0.980
0.370(0.233)(0.734)0.293*
0.752*(0.163)(0.446)0.139
0.354(0.553)(0.220)0.09570.246(0.295)(0.116)0.184
0.0709(0.129)(0.0500)
0.0741
0.191(0.0759)(0.194)0.01580.00604(0.0201)(0.00767)
0.137
0.0528(0.120)(0.305)0.106
0.0408(0.198)(0.0763)-0.000103** -3.94e-05**(4.34e-05)(1.65e-05)
0.350*
0.137*(0.203)(0.0804)
0.791***
0.308***(0.206)(0.0759)-0.333***-0.935***(0.0531)(0.165)-1.392***-0.425***(0.214)(0.0478)-0.291-0.111(0.365)(0.139)-0.171-0.0652(0.106)(0.0406)
3.219*(1.894)

Regular employment

Nonregular employment

Marginal
Coefficient
Coefficienteffect-4.37e-08-0.0944-2.225(1.653)(7.59e-07)(0.444)-2.510*-4.04e-08-0.549(7.05e-07)(0.551)(1.475)0.07050.539
5.28e-07(8.76e-06)(0.585)(0.896)-3.43e-08
1.069-1.268(0.775)(1.471)(6.05e-07)
3.66e-07
0.600
0.488
(5.54e-06)(0.414)(0.675)1.229
1.26e-05
0.0632(0.896)(0.000156)(0.541)-2.39e-08
0.229-0.173(0.276)(3.90e-07)(0.445)0.115-0.00902-1.65e-09(0.125)(0.270)(6.81e-08)-0.179-2.55e-08
0.197(0.353)(4.55e-07)(0.176)0.0442**-0.0707*-1.30e-08(0.0373)(2.18e-07)(0.0185)
6.45e-06-0.583**
1.182***(8.81e-05)(0.278)(0.435)-0.321*1.073***
1.85e-06(2.70e-05)(0.184)(0.321)-6.82e-05*-0.000122**-0(4.03e-05)(6.15e-05)(3.82e-10)-1.67e-08
0.205-0.115(0.372)(2.73e-07)(0.195)1.27e-07
0.643***
0.319(2.17e-06)(0.185)(0.255)-0.425-7.33e-08-0.822***(0.266)(1.25e-06)(0.144)-0.872**-1.02e-07-1.141***(1.72e-06)(0.188)(0.374)0.623*-2.019***-3.72e-07(0.614)(6.30e-06)(0.329)-4.19e-08-0.106-0.227(0.100)(0.167)(7.02e-07)-0.364
3.128(2.578)(1.750)2279

Marginal
effect-0.0208(0.0931)-0.0936(0.0646)
0.0169(0.145)0.361(0.308)0.176(0.145)0.0151(0.133)0.0572(0.0742)0.0272(0.0305)
0.0485(0.0463)0.0102**(0.00430)-0.105***(0.0377)-0.0688*(0.0363)-1.58e-05*(9.40e-06)
0.0515(0.0531)0.186***(0.0636)-0.173***(0.0295)-0.192***(0.0273)
0.144*(0.0768)-0.0245(0.0233)

Voluntary nonregular
employment
Marginal
Coefficienteffect-0.0478-0.248(0.0715)(0.431)-0.0985*-0.654(0.546)(0.0510)-0.229-0.0445(0.567)(0.0956)
0.854
0.268
(0.295)(0.763)0.718*
0.211(0.146)(0.404)0.05370.219(0.523)(0.141)
0.05430.227(0.259)(0.0668)
0.07570.0169(0.0275)(0.120)0.137
0.0315(0.0420)(0.173)0.0472*** 0.0104***(0.00401)(0.0180)-0.557**-0.0960***(0.264)(0.0345)-0.0697**-0.345*(0.0329)(0.178)-4.18e-05-9.20e-06(3.86e-05)(8.53e-06)
0.05220.216(0.201)(0.0530)
0.731***
0.209***(0.181)(0.0621)-0.682***-0.138***(0.0276)(0.140)-0.162***-0.980***(0.177)(0.0253)
0.127*
0.576*(0.0686)(0.309)-0.114-0.0250(0.0967)(0.0214)-0.539(0.0189)(1.691)

Note: 1) The numbers in parentheses are robust standard errors.
2) *, **, and *** indicate statistical significance at the 10, 5, and 1% levels, respectively.

32

Table 5

Estimation results for working hours(1) Without controlling for regional factors
Average weekly working hours
Total sample
Employed
Regular employment(unemployed = 0)
FE
RE
FE
RE
FE
RE
Model regions dummy-0.668-0.289-0.0577-1.7692.062
1.996
× 2007-09 dummy(1.387)(1.357)(2.693)(2.350)(4.066)(4.129)Model regions dummy
1.557
1.975
4.925*
2.913
8.736*
11.35***
× 2010-12 dummy(1.823)(1.857)(2.863)(2.484)(4.802)(3.462)Model regions dummy
1.658-1.379-1.308-1.856-0.468-3.763(3.106)(1.239)(1.812)(1.800)(1.901)(3.183)
2007-09 dummy
1.447**
1.757***-0.996-0.546-2.184-2.435(0.679)(0.649)(1.087)(0.963)(2.670)(2.200)2010-12 dummy-0.5250.156-2.233-0.930-0.824-2.970(0.819)(0.749)(1.413)(1.086)(3.109)(1.970)Age
0.0601-0.0463-0.298-0.418***0.04520.242(0.152)(0.106)(0.234)(0.128)(0.517)(0.214)
University/Graduate school
2.273
4.728***-1.961
graduate dummy(1.561)(1.623)(2.214)
Junior /Technical college-1.372-0.187-1.309graduate dummy(1.061)(1.254)(1.848)Annual income of the spouse -0.000409 -0.000922*** 0.000148 -0.000831*** 0.000535 -0.000793**(0.000281) (0.000194) (0.000443) (0.000259) (0.000792) (0.000398)
Living together dummy
2.820
4.940***-0.6852.948*-1.3650.879(1.944)(1.406)(2.993)(1.736)(6.714)(2.699)Equivalent to the state of
1.117
1.675
-4.317*-2.177-10.17-3.009living together dummy(1.387)(1.068)(2.206)(1.358)(7.514)(3.185)
Number of children under six -4.955*** -5.267***-6.111*** -5.155***-10.39*** -8.862***(0.527)(0.437)(0.893)(0.612)(1.847)(1.129)Constant term
16.83***
23.42***
41.28***
47.22***
43.30***
42.10***(5.083)(3.630)(8.092)(4.473)(16.15)(7.132)
4,002
1,771
544
Observations
R2
0.070
0.067
0.073
0.066
0.153
0.143
Prob>chi2 = 0.0883
Prob>chi2 = 0.2049
Prob>chi2 = 0.7966
Hausman test

Nonregular employment
FE-0.738(3.299)3.270(2.943)0.750(2.770)-0.913(0.907)-3.250**(1.403)0.112(0.257)RE-1.818(2.638)1.634(2.463)0.362(2.100)0.0991(0.785)-1.335(1.012)-0.280**(0.130)1.276(1.842)-1.041(1.106)-0.000256 -0.000709***(0.000496) (0.000253)-1.4430.891(2.219)(1.435)-2.479-1.592(1.951)(1.053)-2.773*** -2.911***(0.885)(0.532)21.64**
36.29***(8.879)(4.410)
1,179
0.028
0.021
Prob>chi2 = 0.6471

Note: 1) The numbers in parentheses are robust standard errors.
2) *, **, and *** indicate statistical significance at the 10, 5, and 1% levels, respectively.
3) FE indicates fixed-effect model, and RE indicates random-effect model.

33

(2)

With controlling for regional factors
Average weekly working hours

Total sample
Employed(unemployed = 0)
FE
RE
FE
RE
-2.338-0.777-0.894-0.821Model regions dummy(3.390)(4.029)(1.943)(1.853)× 2007-09 dummy
3.416
0.130
2.359
1.511
Model regions dummy(4.592)(3.518)(2.883)(2.857)× 2010-12 dummy
0.04860.388-4.402***
1.310
Model regions dummy(1.569)(2.582)(1.794)(3.842)-0.118-1.787
0.868-4.053
2007-09 dummy(1.435)(2.515)(1.297)(0.916)-0.151-0.743-6.899-5.031**
2010-12 dummy(4.419)(1.598)(2.200)(1.254)0.519-0.516***
0.05610.725*
Age(0.737)(0.177)(0.385)(0.152)5.346**
1.846
University/Graduate school(2.587)(2.142)
graduate dummy-0.9170.883
Junior/Technical college(1.573)(1.390)
graduate dummy
Annual income of the spouse -0.000385 -0.000797*** 0.000165 -0.000727*(0.000409) (0.000295) (0.000774) (0.000407)
2.444
4.464**
4.519**
0.687
Living together dummy(2.074)(1.884)(3.509)(2.432)2.743
2.927**-2.326-1.903Equivalent to the state of(1.841)(1.979)(1.483)(3.299)living together dummy-5.634*** -4.856***
Number of children under six -4.694*** -5.175***(0.636)(1.355)(0.805)(0.783)-1.858-11.90***1.485-8.286***
Financial index(3.581)(2.789)(11.52)(6.237)
6.939***
1.684*
2.478*
0.767
ln standard financial scale(0.930)(1.278)(0.757)(2.294)29.34*-48.96**
12.57-104.2**
Constant term
(16.46)(13.66)(45.25)(24.41)
Observations
930
2,190
0.048
0.062
0.054
0.065
R2
Hausman test
Prob>chi2 = 0.3127
Prob>chi2 = 0.0057

Regular employment

Nonregular employment

RE
1.821
(5.847)9.683**(4.822)-3.911(4.805)-4.605(3.468)-4.788(3.061)0.316(0.304)-0.0422(4.065)-3.390(2.552)0.000320 -0.00131*(0.00154) (0.000759)
14.67
2.425
(9.691)(3.475)-16.45*-6.141(4.147)(9.803)-8.599*** -7.559***(2.727)(1.692)-60.54-6.527(5.274)(54.12)-0.8470.242(1.753)(10.75)8.510
44.65(30.21)(206.5)
249
0.206
0.121
Prob>chi2 = 0.1050

RE-3.827(3.350)-2.307(3.284)3.479(2.510)-1.6960.715(1.886)(1.081)-2.891
0.413(3.720)(1.382)-0.547***
0.0910(0.172)(0.630)2.251(2.757)-0.673(1.293)-0.000300 -0.000459(0.000865) (0.000374)-1.6572.479(3.139)(1.804)0.212-0.894(3.141)(1.479)-5.389*** -3.865***(1.422)(0.683)9.050-6.136**(8.302)(3.113)1.415**
4.301***(0.686)(1.652)-57.9324.10*(35.16)(13.57)
653
0.035
0.055
Prob>chi2 = 0.3016

FE
0.700
(4.750)5.390(6.350)-12.08**(4.776)-11.68*(6.249)-20.75*(10.63)
3.164*(1.845)FE-6.176(4.428)-3.947(4.511)

Note: 1) The numbers in parentheses are robust standard errors.
2) *, **, and *** indicate statistical significance at the 10, 5, and 1% levels, respectively.
3) FE indicates fixed-effect model, and RE indicates random-effect model.

34

Table 6

Estimation results for employment: propensity score matching(1) Without controlling for regional factors

2004-06
2007-092010-12

2004-06
2007-092010-12

2004-06
2007-092010-12

2004-06
2007-092010-12

Level
Model
Nonmodelregionsregions
(a)
(b)
0.323
0.414
(0.468)(0.493)0.381
0.498(0.500)(0.487)0.501
0.492(0.500)(0.502)

Level
Model
Nonmodelregionsregions
(b)
(a)
0.110
0.142
(0.313)(0.349)0.064
0.158(0.246)(0.365)0.15
0.183(0.387)(0.359)

Level
Model
Nonmodelregionsregions
(a)
(b)
0.199
0.260
(0.400)(0.439)0.312
0.326(0.464)(0.469)0.342
0.308(0.476)(0.462)

Employment rate
Difference

Difference-in-Differences

Nonmatching

Matching

Nonmatching(a)-(b)-0.091***(0.029)-0.117***(0.038)-0.009(0.049)(a)-(b)-0.080***(0.027)-0.083***(0.035)0.017(0.048)(Difference from 2004-06)-0.026(0.048)0.082(0.057)

Matching-0.003(0.045)0.097**(0.055)

Regular employment rate
Difference

Difference-in-Differences

Nonmatching

Matching

Nonmatching(a)-(b)-0.032(0.020)-0.094***(0.027)-0.033(0.038)(a)-(b)-0.019(0.019)-0.081***(0.018)-0.042(0.034)(Difference from 2004-06)-0.062*(0.033)-0.001(0.041)

Matching-0.062***(0.026)-0.023(0.039)Nonregular employment rate
Difference

Difference-in-Differences

Nonmatching

Matching

Nonmatching(a)-(b)-0.061**(0.026)-0.015(0.036)0.033(0.046)(a)-(b)-0.065***(0.023)0.005(0.034)0.070*(0.047)(Difference from 2004-06)

0.046(0.043)0.094*(0.051)

Matching

0.070**(0.041)0.135***(0.052)

Voluntary nonregular employment rate
Difference
Difference-in-Differences
Level
Model
NonmodelNonmatching MatchingNonmatching Matchingregionsregions(b)(a)-(b)(a)-(b)(a)(Difference from 2004-06)
0.193
0.243
-0.050**-0.053**(0.025)(0.023)(0.395)(0.429)
0.307
0.300
0.006
0.025
0.057
0.078**(0.462)(0.459)(0.035)(0.033)(0.042)(0.041)
0.046
0.081**
0.096*
0.134***
0.333
0.288
(0.473)(0.453)(0.045)(0.046)(0.050)(0.052)

Note: 1) The numbers in parentheses are robust standard errors.
2) *, **, and *** indicate statistical significance at the 10, 5, and 1% levels, respectively.
35

(2) With controlling for regional factors

2004-06
2007-092010-12

2004-06
2007-092010-12

2004-06
2007-092010-12

2004-06
2007-092010-12

Level
Model
Nonmodelregionsregions
(a)
(b)
0.323
0.414
(0.468)(0.493)0.381
0.498(0.487)(0.500)0.492
0.501(0.502)(0.500)

Level
Model
Nonmodelregionsregions
(a)
(b)
0.110
0.142
(0.349)(0.313)0.064
0.158(0.246)(0.365)0.183
0.15(0.359)(0.387)

Model
regions(a)
0.199(0.400)0.312(0.464)0.342(0.476)

Level
Nonmodelregions(b)
0.260(0.439)0.326(0.469)0.308(0.462)

Employment rate
Difference

Difference-in-Differences

Nonmatching

Matching

Nonmatching(a)-(b)-0.091***(0.029)-0.117***(0.038)-0.009(0.049)(a)-(b)0.059*(0.038)0.032(0.044)0.136**(0.064)(Difference from 2004-06)-0.026(0.048)0.082(0.057)

Matching-0.027(0.058)0.077(0.074)

Regular employment rate
Difference

Difference-in-Differences

Nonmatching

Matching

Nonmatching(a)-(b)-0.032(0.020)-0.094***(0.027)-0.033(0.038)(a)-(b)0.041*(0.027)-0.019(0.026)0.072**(0.043)(Difference from 2004-06)-0.062*(0.033)-0.001(0.041)

Matching-0.061*(0.037)0.030(0.051)

Nonregular employment rate
Difference

Difference-in-Differences

Nonmatching

Matching

Nonmatching(a)-(b)-0.061**(0.026)-0.015(0.036)0.033(0.046)(a)-(b)0.020(0.033)0.062*(0.044)0.076(0.060)(Difference from 2004-06)

0.046(0.043)0.094*(0.051)

Matching

0.042(0.055)0.056(0.069)Voluntary nonregular employment rate
Level
Difference
Difference-in-Differences
Model
NonmodelNonmatching MatchingNonmatching Matchingregionsregions(a)(b)(a)-(b)(a)-(b)(Difference from 2004-06)
0.193
0.243
-0.050**0.029(0.395)(0.429)(0.025)(0.033)
0.307
0.300
0.006
0.079**
0.057
0.050(0.462)(0.459)(0.035)(0.044)(0.042)(0.055)
0.096*
0.041
0.333
0.288
0.046
0.070
(0.473)(0.453)(0.045)(0.059)(0.050)(0.068)

Note: 1) The numbers in parentheses are robust standard errors.
2) *, **, and *** indicate statistical significance at the 10, 5, and 1% levels, respectively.
36

Figure 1

Changes in the employment rates of women in Japan(1) Employment rate(2) Regular employment rate(3) Non-regular employment rate(4) Voluntary non-regular employment
rateNote) The vertical line in each of the figures indicates the 95% confidence interval.

37